Cloud, CI/CD, monitoring, and deployment (7.1K skills)
Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).
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/cs:ai-act-readiness <system> — EU AI Act 6-question forcing interrogation. Use during AI-system intake, before EU deployment, or during annual compliance refresh as Article 113 obligations phase in (2025-02-02 / 2025-08-02 / 2026-08-02 / 2027-08-02).
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Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.
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Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
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Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
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Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
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Review a planned deployment or release diff for rollback feasibility, mixed-version compatibility, migrations, and configuration readiness without deploying it.
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Verify that a requested bug fix changes observable behavior with a failing-before, passing-after reproduction and honest execution evidence.
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deploy the Wasp app to Railway or Fly.io using Wasp CLI.
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Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.
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Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.
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Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.
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Skill for building with the Astro web framework. Helps create Astro components and pages, configure SSR adapters, set up content collections, deploy static sites, and manage project structure and CLI commands. Use when the user needs to work with Astro, mentions .astro files, asks about static site generation (SSG), islands architecture, content collections, or deploying an Astro project.
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Expert-level engineering thermodynamics covering laws of thermodynamics, thermodynamic cycles, steam power plants, refrigeration, gas dynamics, and combustion.
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Expert-level volcanology covering volcanic processes, eruption styles, volcano monitoring, volcanic hazards, igneous petrology, and volcanic contributions to climate.
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Expert-level thermodynamics knowledge. Use when working with heat, temperature, entropy, thermodynamic laws, heat engines, refrigerators, phase transitions, statistical mechanics, or thermodynamic cycles. Also use when the user mentions 'entropy', 'enthalpy', 'Carnot', 'heat engine', 'thermodynamic cycle', 'ideal gas', 'phase transition', 'Gibbs free energy', 'specific heat', 'thermal equilibrium', or 'second law'.
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Expert-level classical mechanics knowledge. Use when working with Newton's laws, kinematics, dynamics, energy, momentum, rotational motion, oscillations, gravitation, Lagrangian mechanics, or Hamiltonian mechanics. Also use when the user mentions 'force', 'torque', 'momentum', 'energy conservation', 'simple harmonic motion', 'projectile', 'friction', 'center of mass', 'moment of inertia', 'Lagrangian', or 'Hamiltonian'.
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Apple's native `container` CLI for running Linux containers as lightweight per-container VMs on macOS 26 Apple Silicon. Load when creating, configuring, managing, backing up, sharing, or deleting containers, images, volumes, or networks with `container`; when `container` or `container system start` fails; or when choosing between `container` and Docker/Podman/Colima on a Mac.
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Retrieve current documentation, API references, and code examples for any library, framework, SDK, CLI tool, or cloud service — including well-known ones like React, Next.js, Prisma, Django, or Spring Boot. Use for API syntax, configuration options, version migrations, setup instructions, and library-specific debugging, even when you already think you know the answer: training data on API details and signatures is frequently outdated. Prefer this over web search for library documentation.
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Claude Code workflow tips from Boris Cherny (creator of Claude Code) and the Claude Code team. Use when: setting up Claude Code, optimizing workflows, running parallel sessions, configuring CLAUDE.md, using skills/commands, subagents, hooks, MCP integrations, or learning best practices. Covers: git worktrees, plan mode, verification, permissions, Slack MCP, BigQuery, prompting tips, plugins, custom agents, sandboxing, keybindings, status lines, output styles, customization, /simplify for code quality, /batch for parallel code migrations, /loop for scheduled tasks, code review agents, /btw for mid-task questions, /effort max reasoning, remote control sessions, voice mode, setup scripts, session naming, /color, PostCompact hook, auto mode, /schedule cloud jobs, iMessage plugin, auto-memory, and auto-dream, mobile app, session teleporting, Cowork Dispatch, Chrome extension, Desktop app, /branch forking, --bare SDK startup, --add-dir multi-repo, --agent custom agents, /voice input.
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Use when adding logging, metrics, tracing, or alerting to production code, or when a past incident lacks diagnostic data.
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Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.
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LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.
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Go / no-go gate before any deployment. Checks failure modes, rollback plan, cost, production bar, and definition of done. Trigger before releasing to any environment that carries real consequences.
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